An edge computing (EC) network extends the notion of cloud computing concept to perform its capability to the network's edge. The main aims of EC are almost similar to cloudlets or fog computing in other references. Moreover, EC offers data storage, and computational processing is locally performed in the edge infrastructure to be closer to the data provider or user. As a result, edge computing offers real-time services, location-aware, and low-latency communication. Further, it also reduces delay and saves the bandwidth of transferring data for the remote node in the vehicular network system.
Moreover, current efforts utilized FL to improve the usability of MEC in reaching edge intelligence systems for wireless networks. In [1]}, the authors explored an FL model over wireless networks to improve FL's activities utilizing a control algorithm approach by respecting energy consumption as well as communication and computation latency. The authors in [2]} focus on improving system performance and solving the FL loss function problem during the training phase by optimizing resource allocation and user selection mechanisms. Further, the authors in [3]} deployed FL to form collaborative edge intelligence in mitigation situations of vehicular cyber-physical systems for detecting data leakage and protecting user's privacy information.
